Consider the following unobserved effects model: Yit =B₁ xit1 +...+ßk¤itk + ai + Uit You plan on using fixed effects to estimate the model. Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, ¿ = 3, individual in your data set. Your panel data set has total observations. Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that a; is assumed to be uncorrelated with the idiosyncratic error, uit. This correlation cause the fixed effects estimators to be biased.
Consider the following unobserved effects model: Yit =B₁ xit1 +...+ßk¤itk + ai + Uit You plan on using fixed effects to estimate the model. Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, ¿ = 3, individual in your data set. Your panel data set has total observations. Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that a; is assumed to be uncorrelated with the idiosyncratic error, uit. This correlation cause the fixed effects estimators to be biased.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Consider the following unobserved effects model:
Yit = ₁xit1 +...+ ßkXitk + Aį + Uit
You plan on using fixed effects to estimate the model.
Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, i = 3,
individual in your data set.
Your panel data set has
total observations.
Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that ai is
assumed to be uncorrelated with the idiosyncratic error, uit.
This correlation
cause the fixed effects estimators to be biased.
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